ArticleJournal of medical Internet research2021
COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis.Journal of medical Internet research · 2025Article
- People's Attitude and Perception of the Pandemic on Twitter: A Case Study of COVID-19 in India.Cureus · 2024Article
- Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter.Preventive medicine · 2023Article
- Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.Health information science and systems · 2023Article
- User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis.Journal of medical Internet research · 2023Article
- COVID-19-Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages.JMIR infodemiology · 2023Article
- A corpus-based pragmatic analysis of Jordanians Facebook status updates during COVID-19.Ampersand (Oxford, UK) · 2023Article
- Infodemics during era of COVID-19 pandemic: A review of literature.Journal of family medicine and primary care · 2022Review
- Readability of Korean-Language COVID-19 Information from the South Korean National COVID-19 Portal Intended for the General Public: Cross-sectional Infodemiology Study.JMIR formative research · 2022Article
- Article
- Measuring the Evolution of Risk Communication Strategy for Health Authorities During the COVID-19 Pandemic: An Empirical Comparison Between China and the United States.International journal of public health · 2022Article
- Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data.International journal of environmental research and public health · 2021Article
- How to Make Health and Risk Communication on Social Media More "Social" During COVID-19.Risk management and healthcare policy · 2021Article
- Article
- Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook.JMIR infodemiologyArticle
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic.
objectiveThis research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India.
methodsWe collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time-topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings.
resultsThis research found that each government's official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity.
conclusionsThis paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.
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